mirror of
https://github.com/bnair123/MusicAnalyser.git
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a97997a17a
- Created FastAPI backend structure. - Implemented Spotify Recently Played ingestion logic. - Set up SQLite database with SQLAlchemy models. - Added AI Service using Google Gemini. - Created helper scripts for auth and background worker. - Added Dockerfile and GitHub Actions workflow.
40 lines
1.3 KiB
Python
40 lines
1.3 KiB
Python
from sqlalchemy import Column, Integer, String, DateTime, JSON, ForeignKey, Boolean
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from sqlalchemy.orm import relationship
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from datetime import datetime
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from .database import Base
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class Track(Base):
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__tablename__ = "tracks"
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id = Column(String, primary_key=True, index=True) # Spotify ID
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name = Column(String)
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artist = Column(String)
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album = Column(String)
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duration_ms = Column(Integer)
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popularity = Column(Integer, nullable=True)
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# Store raw full JSON response for future-proofing analysis
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raw_data = Column(JSON, nullable=True)
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# AI Analysis fields
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lyrics_summary = Column(String, nullable=True)
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genre_tags = Column(String, nullable=True) # JSON list stored as string or just raw JSON
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created_at = Column(DateTime, default=datetime.utcnow)
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updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
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plays = relationship("PlayHistory", back_populates="track")
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class PlayHistory(Base):
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__tablename__ = "play_history"
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id = Column(Integer, primary_key=True, index=True)
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track_id = Column(String, ForeignKey("tracks.id"))
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played_at = Column(DateTime, index=True) # The timestamp from Spotify
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# Context (album, playlist, etc.)
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context_uri = Column(String, nullable=True)
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track = relationship("Track", back_populates="plays")
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